In this paper, a new fractional-order (FO) PI^λD^µ controller is designed with the desired gain and phase margin for the automatic rudder of underactuated surface vessels (USVs). The integral order λ and the differential order μ are introduced in the controller, and the two additional adjustable factors make the FO PI^λD^µ controller have better accuracy and robustness. Simulations are carried out for comparison with a ship’s digital PID autopilot. The results show that the FO PI^λD^µ controller has the advantages of a small overshoot, short adjustment time, and precise control. Due to the uncertainty of the model parameters of USVs and two extra parameters, it is difficult to compute the parameters of an FO PI^λD^µ controller. Secondly, this paper proposes a novel particle swarm optimization (PSO) algorithm for dynamic adjustment of the FO PI^λD^µ controller parameters. By dynamically changing the learning factor, the particles carefully search in their own neighborhoods at the early stage of the algorithm to prevent them from missing the global optimum and converging on the local optimum, while at the later stage of evolution, the particles converge on the global optimal solution quickly and accurately to speed up PSO convergence. Finally, comparative experiments of four different controllers under different sailing conditions are carried out, and the results show that the FO PI^λD^µ controller based on the IPSO algorithm has the advantages of a small overshoot, short adjustment time, precise control, and strong anti-disturbance control.

Aiming at the uncertainty problem caused by the time-varying modeling parameters associated with ship speed in the course tracking control of underactuated surface vessels (USVs), this paper proposes a control algorithm based on the dynamic neural fuzzy model (DNFM). The DNFM simultaneously adjusts the structure and parameters during learning and fully approximates the inverse dynamics of ships. Online identification and modeling lays the model foundation for ship motion control. The trained DNFM, serving as an inverse controller, is connected in parallel with the fractional-order PI^λD^μ controller to be used for the tracking control of the ship’s course. Moreover, the weights of the model can be further adjusted during the course tracking. Taking the actual ship data of a 5446 TEU large container ship, simulation experiments are conducted, respectively, for course tracking, course tracking under wind and wave interferences, and comparison with five different controllers. This proposed controller can overcome the influence of the uncertainty of modeling parameters, tracking the desired course quickly and effectively.

To solve the problems of leakage and low detection accuracy in the detection of different types of diseases in rice leaves, this paper proposes the SPW YOLOv8 (SPD-Conv-RPSA-WIoU-YOLOv8) detection algorithm, which is built based on the YOLOv8 structure, and synergistically optimizes the feature extraction, attention, and loss design to improve the detection effect and generalization ability. First, a Sparse Depthwise Convolution (SPD-Conv) is used to replace the original convolution to enhance the extraction of low-resolution and small-target features, better adapting to the needs of small-scale and overlapping scene detection of leaves; Then, the RPSA attention module is proposed to optimize the design for the potential correlation between the channel and the spatial features, and the ReLU activation function is employed to introduce a learnable scaling factor to improve the efficiency of the resource allocation and significantly enhance the ability to detect complex backgrounds and tiny targets. Finally, the WIoU v3 loss function is adopted to optimize the detection convergence process and enhance detection accuracy, thereby improving the detection effect. Experiments demonstrate that SPW YOLOv8 achieves 90.2% mAP, 88.0% recall, and 90.2% detection accuracy in the task of detecting rice leaf diseases, which are 2.0%, 5.9%, and 3.7% higher than YOLOv8, respectively. It performs better relative to YOLOv5, YOLO-World, YOLOv11, RT-DETR, and SSD detection algorithms, showing higher detection capability and effectiveness, highlighting the significance of the SPW-YOLOv8 model in improving the detection precision and effectiveness in real agricultural scenarios.